Resistance Gene Association and Inference Network (ReGAIN): A Bioinformatics Pipeline for Assessing Probabilistic Co-Occurrence Between Resistance Genes in Bacterial Pathogens.
Resistance Gene Association and Inference Network (ReGAIN): A Bioinformatics Pipeline for Assessing Probabilistic Co-Occurrence Between Resistance Genes in Bacterial Pathogens.
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耐药基因关联和推理网络 (ReGAIN)：用于评估细菌病原体中耐药基因之间的概率共现的生物信息学管道。
DOI：
10.1101/2024.02.26.582197
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Winter,JaclynM
中科院分区：
文献类型：
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作者：
Horvath,ElijahRBring;Stein,MathewG;Mulvey,MatthewA;Hernandez,EdgarJ;Winter,JaclynM
多药耐药(MDR)细菌病原体的猖獗增长对健康构成了严重威胁，需要创新工具来揭开抗菌素耐药性的复杂遗传基础。尽管在开发用于检测抗性基因的基因组工具方面取得了重大进展，但在分析抗性基因共同出现的特定生物体模式方面仍然存在差距。针对这一不足，我们开发了抗性基因关联和推理网络(REAIN)，这是一个新型的基于网络的命令行基因组平台，使用贝叶斯网络结构学习来识别和定位细菌病原体中的抗性基因网络。REAIN不仅使用成熟的方法检测耐药基因，而且还阐明了它们复杂的相互作用，这对于理解MDR表型至关重要。以ESKAPE病原体为重点，REAIN产生了一个可查询的数据库，用于研究耐药基因的共生现象，丰富耐药组分析，并为抗菌素耐药性的动态变化提供新的见解。此外，REGain的多功能性不仅包括抗生素耐药基因，还包括评估重金属耐药和毒力决定因素之间的共生模式，提供影响疾病进展和治疗结果的关键基因关系的全面概述。
The rampant rise of multidrug resistant (MDR) bacterial pathogens poses a severe health threat, necessitating innovative tools to unravel the complex genetic underpinnings of antimicrobial resistance. Despite significant strides in developing genomic tools for detecting resistance genes, a gap remains in analyzing organism-specific patterns of resistance gene co-occurrence. Addressing this deficiency, we developed the Resistance Gene Association and Inference Network (ReGAIN), a novel web-based and command line genomic platform that uses Bayesian network structure learning to identify and map resistance gene networks in bacterial pathogens. ReGAIN not only detects resistance genes using well-established methods, but also elucidates their complex interplay, critical for understanding MDR phenotypes. Focusing on ESKAPE pathogens, ReGAIN yielded a queryable database for investigating resistance gene co-occurrence, enriching resistome analyses, and providing new insights into the dynamics of antimicrobial resistance. Furthermore, the versatility of ReGAIN extends beyond antibiotic resistance genes to include assessment of co-occurrence patterns among heavy metal resistance and virulence determinants, providing a comprehensive overview of key gene relationships impacting both disease progression and treatment outcomes.